Paper learns an optimization algorithm for neural nets.
problem High-dimensional stochastic optimization problems in neural nets.
method Reinforcement learning framework to learn optimization algorithms.
result Learned optimization algorithm outperforms known algorithms.
CSVAE learns interpretable latent subspaces for binary labels.
problem Learning interpretable latent representations correlated to specific labels.
method Conditional Subspace VAE (CSVAE) using mutual information minimization.
result CSVAE extracts interpretable latent subspaces for binary labels.
Research uses CPS to estimate uncertainty in ML radio metric models.
problem Estimating uncertainty in machine learning models for radio metrics and path loss.
method Conformal Prediction (CP) in Conformal Predictive Systems (CPS) with diverse difficulty estimators.
result CPS models maintain high coverage and reliability across different cities.
We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer perceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Training a generative adversarial network…
New methods predict ambulance demand with high accuracy.
problem Accurate prediction of ambulance demand in urban areas.
method Three methods based on Gaussian mixture models, kernel density estimation, and kernel warping.
result Significantly more accurate predictions for Toronto and Melbourne.
The study examines how brokers' identity affects their trading strategies on the Toronto Stock Exchange.
problem Impact of anonymous trading on brokers' optimal execution strategies.
method Formulated a stochastic differential game and mean-field game to analyze the optimal execution problem of anonymous and identity-revealed trading.
result Obtained a closed-form solution for the optimal strategy under Almgren-Chris price impact framework.
Deep neural networks predict walking, biking, and driving from Wi-Fi signals.
problem Predicting human mobility modes using Wi-Fi signals.
method Deployed Wi-Fi sensors at four locations, developed and tested multiple classifiers (MLP, Decision Tree, Bagged Decision Tree, Random Forest).
result Multilayer Perceptron achieved 86.52% correct predictions of mobility modes.
We introduce a new invariant of tangles along with an algebraic framework in which to understand it. We claim that the invariant contains the classical Alexander polynomial of knots and its multivariable extension to links. We argue that of the computationally efficient members of the family of Alexander invariants, it…
Lecture notes from the Concentrated Graduate Course preceding the Workshop on Hodge Theory in String Theory at the Fields Institute in Toronto, November 11--15, 2013.
These condensed notes treat some basic notions in Tropical Geometry (varieties, cycles, modifications, equivalence). These topics are to be extended, illustrated and included to the upcoming book project http://www.math.toronto.edu/mikha/book.pdf .
The paper analyzes optimal execution strategies for traders with inventory processes influenced by Brownian motion.
problem Optimal execution strategies for traders with inventory processes influenced by Brownian motion.
method Statistical tests and empirical analysis of intra-day data from the Toronto Stock Exchange.
result Empirical evidence supports the presence of a non-zero Brownian motion component in inventories and wealth processes.
Unified model for age-invariant face recognition with photorealistic face synthesis.
problem Reliable face recognition across ages remains challenging due to significant intra-class variations.
method Unified deep architecture for cross-age face synthesis and recognition, continuous face rejuvenation/aging, disentangled age-invariant face representations.
result Superior performance on CAFR and other cross-age datasets, promising generalizability to unconstrained face recognition.
Deep learning predicts road GHG emissions with speed, density, and past ERs.
problem Predicting GHG emissions from road networks to mitigate environmental impact.
method Developed a deep learning framework using LSTM networks with exogenous variables.
result LSTM with speed, density, GHG ER, and in-links speed from previous minutes performs best.
Paper proposes using synthetic data to improve face recognition accuracy.
problem Improving face recognition accuracy using real data alone.
method Proposes a GAN that disentangles identity attributes and generates photo-realistic synthetic images.
result Synthetic images generated by the model are photo-realistic and can increase face recognition accuracy.
Paper compares neural networks and time-series models for weather derivative pricing.
problem Pricing accuracy and regime adaptation for temperature and precipitation weather derivatives.
method Benchmarked harmonic-regression/ARMA vs. feed-forward neural network for temperature. Used CNN for precipitation, adapting to seasonal heterogeneity.
result CNN yields more accurate pricing, especially for regime-adapted seasonal data.
Paper proposes a new embedding method for face verification and clustering.
problem Unconstrained face verification remains challenging.
method Coupling deep CNN with triplet probability constraints for low-dimensional embedding.
result The proposed method outperforms state-of-the-art methods in verification and identification metrics.
This note explores comparison geometry concepts and theorems.
problem Exploring various comparison theorems in geometry.
method Analyzes Rauch and Toponogov theorems and their applications.
result Introduction of Gromov-Hausdorff convergence and Alexandrov Spaces.
Deep learning detects face swapping with high accuracy and uncertainty.
problem Photo-realistic face swapping without consent.
method Deep transfer learning for face swapping detection, human subject rankings for comparison.
result True positive rates >96% with minimal false alarms, uncertainty provided for each prediction.
Simple method detects deepfakes with few labeled samples.
problem Automated detection of AI-generated fake images.
method Frequency domain analysis followed by basic classifier.
result Achieved 100% accuracy in high-resolution face images with minimal training data.
Pixel-wise relevance method shows how CNNs classify faces, varying across datasets and tasks.
problem Interpreting black-box CNN face recognition models.
method Layer-wise relevance propagation (LRP) applied to VGG-16 models trained for face recognition.
result Relevance maps are generally stable across random initializations and tasks, but less so across pretraining datasets.
Optimizes master faces for 2D and 3D face verification using evolutionary algorithms and neural networks.
problem Impersonation attacks using master faces for face-based identity authentication.
method Evolutionary algorithm in latent space of StyleGAN, neural network to direct search, 2D and 3D face reconstruction.
result Obtains high impersonation rates with fewer master faces for 2D and 3D face verification.
This paper evaluates power consumption and inference time for face emotion recognition on embedded systems.
problem Lack of information on power consumption and inference time for face emotion recognition on embedded systems.
method Identified state-of-the-art methods, collected new dataset, evaluated on three embedded devices.
result Gray images are more suitable for embedded systems, and power consumption and inference time are limiting factors.
Generates realistic faces from detailed textual descriptions.
problem Face generation from fine-grained textual descriptions.
method Conditional GAN model with DC-GAN and GAN-CLS loss, using CelebA dataset with generated captions.
result Promising results in generating diverse face images from fine-grained textual descriptions.
Universal adversarial patches prevent face detection in various frameworks.
problem Preventing face detection in state-of-the-art face detection systems.
method Investigated the phenomenon of patches that suppress face detection and proposed optimization-based approaches for automatic design.
result Universal adversarial patches can prevent face detection without introducing false positives.
KaoKore dataset extracts faces from pre-modern Japanese art for machine learning.
problem Lack of relevant datasets for historical Japanese artworks in machine learning.
method Extracted faces from pre-modern Japanese artwork to create a new dataset.
result Demonstrated the dataset's value for image classification and creative applications.
Contrastive examples improve fairness in face recognition by balancing minority and majority groups.
problem Face recognition algorithms favor majority groups in training data.
method Create contrastive examples by swapping group memberships in the training dataset.
result Contrastive examples improve fairness metrics like equalized odds.
Face recognition system trained with noisy labels.
problem Label noise in training deep learning classifiers.
method Review and apply recent methods to manage noisy annotations.
result Improved performance of face recognition system with noisy labels.
Regularization is essential when training large neural networks. As deep neural networks can be mathematically interpreted as universal function approximators, they are effective at memorizing sampling noise in the training data. This results in poor generalization to unseen data. Therefore, it is no surprise that a ne…
New algorithm disentangles latent space in GANs using video sequences.
problem Learning disentangled latent spaces in GANs without supervision.
method Adversarial training with video sequences, modifying standard GAN algorithm.
result Disentangled latent space into content and motion attributes.
Enhances 2D face recognition with 3D features using active illumination.
problem Improving robustness of 2D face recognition to spoofing attacks and low-light conditions.
method Projecting a high spatial frequency pattern onto the face to recover 3D information and a 2D image simultaneously.
result Significantly boosts face recognition performance and dramatically improves robustness to spoofing attacks.
Uses simulations to detect AI bias in face detection.
problem Bias in machine learning models can lead to poor performance on minority groups.
method Bayesian parameter search to identify weaknesses in ML classifiers.
result Identifies demographic biases in commercial face APIs.
GANs can bias synthetic data, affecting minority and female faces.
problem GANs can amplify biases in synthetic data augmentation.
method Examine GANs on face-shots with gender and skin tone biases.
result GANs generate biased synthetic data, skewing minority modes and features.
Deep neural networks improve face matching across different domains for banking security.
problem Matching facial images from ID documents with selfies for secure transactions.
method A novel deep learning architecture using two CNNs for cross-domain face matching.
result Accuracy rates higher than 93% on the FaceBank dataset.
New method improves Frank-Wolfe for low-rank matrix completion.
problem Low-rank matrix completion problem.
method Extended Frank-Wolfe method with in-face directions.
result Significant speed-ups in computing very low-rank solutions.
Study analyzes carbon footprint of 1,417 ML models on Hugging Face.
problem Scarce knowledge on measuring and reporting carbon footprint of ML models.
method Repository mining study on Hugging Face Hub API.
result Stalled carbon emissions-reporting models, slight decrease in carbon footprint over 2 years.
Paper proposes tensor sparse PCA for improved face recognition accuracy.
problem Face recognition accuracy improvement using novel methods.
method Combines tensor sparse PCA with nearest-neighbor and kernel ridge regression methods.
result Tensor sparse PCA method yields better accuracy than PCA method alone.
Unified tensor model disentangles object appearance factors.
problem Representing hierarchical intrinsic and extrinsic causal factors of object appearance.
method Compositional hierarchical tensor factorization.
result Interpretable object representation robust to occlusion and reduced training data requirements.
UR-FUNNY dataset aids in understanding multimodal humor.
problem Understanding humor in a multimodal context is understudied.
method Developed a multimodal dataset (UR-FUNNY) for humor detection.
result UR-FUNNY opens the door to multimodal humor detection research.
MTL method uses unlabeled data with pseudo labels to improve classification with disjoint datasets.
problem Improving classification performance with disjoint labeled datasets using unlabeled data.
method Proposes MTL-SA method to select and augment unlabeled data with confident pseudo labels and close distribution to labeled data.
result Extensive experiments show the effectiveness of MTL-SA method in improving classification performance.
Riemannian neural networks outperform standard methods on various datasets.
problem Improving neural network training efficiency and performance.
method Quasi-diagonal Riemannian gradient descent.
result Quasi-diagonal Riemannian algorithms consistently outperform simple stochastic gradient descent.
Improved autoencoder for realistic face manipulation.
problem Face identity conservation and disentanglement in high-resolution images.
method Modified progressively growing autoencoder (PIONEER) with new normalization schemes.
result Significantly improved visual and quantitative results for face identity conservation.
Study examines strengths and weaknesses of deep learning models in face recognition.
problem Improving deep learning models for face recognition under degraded conditions.
method Experimentation on four deep CNN models using LFW dataset, investigating effects of image quality and model characteristics.
result Noise, blur, and missing pixels negatively impact face verification performance; contrast changes and compression artifacts have limited effect.
This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.
problem Creating large datasets for deep learning in computer vision.
method 3D facial modelling techniques and deep learning methodologies.
result Synthetic 3D thermal facial data created for deep learning applications.
Paper quantifies dataset shift for credit card fraud detection.
problem Change in purchase behavior over time affects fraud detection accuracy.
method Measures day-to-day dataset shift using classification efficiency and clustering.
result Improves credit card fraud detection by incorporating dataset shift knowledge.
Max pooling selects frames with similar poses for better face recognition.
problem Measuring similarity of faces with varying poses in videos.
method Pose-Selective Max Pooling: Select frames closest to centroid of K-means cluster.
result Max correlation among selected features yields better performance than VGG-face.
Paper proposes a framework to protect user anonymity in emotion recognition.
problem Preserving user anonymity in face-based emotion recognition systems.
method Adversarial learning framework using CNN architecture.
result The proposed approach minimizes identity-specific information and maximizes emotion-dependent information.
New Siamese Capsule Networks improve face verification in limited data settings.
problem Few-shot face verification with limited labeled data.
method Siamese Capsule Networks with contrastive loss and normalized pose features.
result Siamese Capsule Networks outperform baselines in few-shot face verification.
Paper proposes IE loss for deep metric learning improving CNN performance.
problem Improving deep learning models' performance in classification tasks.
method IE loss method to force distance between samples and class centers.
result IE loss leads to great improvements on various datasets.